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Record W3202676000 · doi:10.1038/s41409-021-01450-3

An adapted European LeukemiaNet genetic risk stratification for acute myeloid leukemia patients undergoing allogeneic hematopoietic cell transplant. A CIBMTR analysis

2021· article· en· W3202676000 on OpenAlexafffund
Antonio Jiménez, Marcos de Lima, Krishna V. Komanduri, Trent Wang, Mei-Jie Zhang, Karen Chen, Hisham Abdel‐Azim, Muhammad Bilal Abid, Mahmoud Aljurf, Hassan B. Alkhateeb, Amer Assal, Ulrike Bacher, Frédéric Baron, Minoo Battiwalla, Amer Beitinjaneh, Nelli Bejanyan, Vijaya Raj Bhatt, Michael Byrne, Jean‐Yves Cahn, Mitchell S. Cairo, Paul Castillo, Edward A. Copelan, Zachariah DeFilipp, Miguel Ángel Díaz, Mahmoud Elsawy, Robert Peter Gale, Biju George, Michael R. Grunwald, Gerhard Hildebrandt, William J. Hogan, Christopher G. Kanakry, Ankit Kansagra, Mohamed A. Kharfan‐Dabaja, Nandita Khera, Maxwell M. Krem, Aleksandr Lazaryan, Joseph Maakaron, Rodrigo Martino, Joseph P. McGuirk, Fotios V. Michelis, Giuseppe Milone, Asmita Mishra, Hemant S. Murthy, Alberto Mussetti, Sunita Nathan, Taiga Nishihori, Richard F. Olsson, Neil Palmisiano, Sagar S. Patel, Ayman Saad, Sachiko Seo, Akshay Sharma, Melhem Solh, Leo F. Verdonck, Baldeep Wirk, Jean A. Yared, Mark R. Litzow, Partow Kebriaei, Christopher S. Hourigan, Wael Saber, Daniel J. Weisdorf

Bibliographic record

VenueBone Marrow Transplantation · 2021
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsPrincess Margaret Cancer CentreDalhousie University
FundersNational Institute of Allergy and Infectious DiseasesNational Cancer InstituteNational Heart, Lung, and Blood InstituteFred and Pamela Buffett Cancer CenterMarkey Cancer Center, University of KentuckyJuno TherapeuticsOffice of Naval ResearchIrving Medical Center, Columbia UniversitySanofi GenzymeLegend BiotechU.S. NavyUniversity of Florida HealthKite PharmaHealth Resources and Services AdministrationNational Institutes of HealthUniversity of Nebraska Medical CenterChristian Medical College, VelloreBiomedical Advanced Research and Development AuthorityUniversity of MiamiKing Faisal Specialist Hospital and Research CentreAstellas PharmaDaiichi-SankyoGilead SciencesDalhousie UniversityImperial College LondonCardinal HealthInselspital, Universitätsspital BernKiadis PharmaPharmacyclicsAlexion Pharmaceuticalsbluebird bioDaiichi Sankyo EuropeTakeda OncologyAgios PharmaceuticalsAngiocrine BioscienceUniversité Grenoble AlpesJazz PharmaceuticalsOmeros CorporationVertex PharmaceuticalsLeonard M. Miller School of MedicineUniversity of BernU.S. Department of DefenseMedical College of WisconsinStemCyteMassachusetts General HospitalCareDxBeiGeneVanderbilt University Medical CenterActinium PharmaceuticalsUniversity of Texas Southwestern Medical CenterCSL BehringChildren's Hospital Los AngelesCelgeneFalk FoundationMoffitt Cancer CenterSanofiIncytePfizerBristol-Myers SquibbGenentechAstraZenecaUniversity of Southern CaliforniaAstellas Pharma USBe The Match FoundationNovartis Pharmaceuticals CorporationAmgenVanderbilt University
KeywordsMedicineInternal medicineUnivariate analysisTransplantationOncologyMyeloid leukemiaMultivariate analysisHematopoietic stem cell transplantationSurgery

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0110.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.262
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations26
Published2021
Admission routes2
Has abstractno

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